Network operators and communication service providers typically rely on various network virtualization technologies to manage complex, large-scale data centers, which may include a multitude of network computing devices (e.g., servers, switches, routers, etc.) to process network traffic through the data center. For example, network operators and service provider networks may rely on network function virtualization (NFV) deployments to deploy network services (e.g., firewall services, network address translation (NAT) services, deep packet inspection (DPI) services, evolved packet core (EPC) services, mobility management entity (MME) services, packet data network gateway (PGW) services, serving gateway (SGW) services, billing services, transmission control protocol (TCP) optimization services, etc.). To provide scalability to meet network traffic processing demands and reduce operational costs, virtual network functions (VNFs) are typically employed to handle specific network function operations. Such operations are typically run on one or more virtual machines (VMs) in a virtualized environment on top of the hardware networking infrastructure.
Data flows occurring between such VNFs (i.e., inter-VNF flows) are commonly optimized by inter-VM shared memory (IVSHMEM), relying on cache memory to provide critical speed advantages. However, latency can vary unpredictably under multi-VM consolidation, which is common in NFV deployments. Accordingly, development of interfacing applications generally requires careful design to ensure that critical accesses will be cache supported. However, in embodiments in which multiple VNFs are deployed and each VNF relies on one or more VMs sharing the same cache, minimizing the impact of one VM on the other VMs can be difficult to achieve.